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Episode #157: Alberto Cairo

Sep 3
·
30 minutes

isusing data and charts to sort of prove quotation marks in there
that African Americans target whites more often than whites target African
Americans when it comes to committing a crime or when white, when black
criminals target white people, that happens more often than white criminals
targeting black people. And I’m not going to get into the details of why, you
know, these, all these graphics are, are basically um, crap. Uh, I describe
that in a lot of detail in the book. Um, but it’s like it’s a very sad story
and actually demonstrates that bad charts can sometimes have terrible
consequences. So this guy Roof will be a racist regardless of the existence of
these graphics I believe because he was a racist since he was a, since he was a
child. But I think that the charts contributed to basically ground his beliefs
and strengthen his beliefs even more. So cases like these are certainly
infuriating and there are a few other charts in the book that I believe that
were designed intentionally to mislead people. And I called them out obviously,
but most of the examples in the book are examples of charts that are otherwise
perfectly designed, but they are often misread or misinterpreted. And this is
not infuriating. It’s just a fact of life. I mean, we are taught, you know, a
or we are, we are told that, you know, we should be able to intuitively
understand visualization that visualization is easy to read, that a picture is
worth a thousand words and things like that. And in the book I tried to
demonstrate that all these myths are actually myths and that we need to abandon
them that visualization is sort of like a like written language. You need to
pay attention to it. You need to read it carefully, uh, in order to interpret
it, uh, in order to interpret.
JS: Yeah. Yeah. I’m always
surprised when I show people, you know, a different type of graph. You know, like,
you know what we, you know, like a slope chart or a dot plot that we have of,
you know, that we in the field know now instinctively. And people say I can’t
show this to my boss or my manager or whatever because they’ll never understand
it. And I find that interesting because it’s not like we know instinctively in
our DNA how to read a bar chart. We have to learn how to read a bar chart.
AC: Yeah. I, I perfectly remember
when I learned to use a, to, to read a scatterplot for example, and it was not
intuitive. I needed to pay attention to the chart. Take a look at the axis, um,
read a little description, read a little caption that the chart had in order to
interpret it correctly. So in the book I talk about visual literacy obviously.
There is a term to refer to visualization literacy. The term is graphicacy. And
I explained where, where that term comes from and other authors that have used
it in the past. And I said that the problem is that we like graphicacy and but
we can increase graphicacy. The, the problem with, you know, people who react
negatively to novel graphic forms is that they say, well, my reader is not
going to understand this chart. So they refrain from using that chart. But
that’s the wrong response. The wrong response is to say, well, if this is the
best chart to represent your data or to tell or to convey the message that you
want to convey, do use it. Go ahead and use it, but also explain how to read it
right to help guide your readers by the hand in order for them to understand
what’s going on in that chart. And then the next time that they see that same
type of chart, they would be able to read it on their own. You have, you would
have increased their graphicacy.
JS: Yeah. Yeah, absolutely. So,
so you talked about a couple of these examples where the graph was
intentionally misleading or misrepresenting the data. What’s the balance
between those type of charts that are intentionally misleading versus one that,
that use bad data visualization techniques?
AC: Well, in the book itself, in
How Charts Lie, I would say that 20, 25% of the examples are charts that I
guess, it’s just a guess that I guess that are intentionally misleading. Um,
and the other 75% are charts that are either well-designed, but misinterpreted
anyway, or charts that are designed with good intentions in mind, but that they
employ, you know, visualization techniques that are not appropriate for a
particular audience. And as a consequence of that, they end up being misleading
any way. The result is [indiscernible 00:10:46]. The audience, audience that is
reading their graphic is misled. So I would say that that’s sort of the sort of
the balance because again, I’m much more interested not in the intentions of
the designers who create those charts. I’m much more interested in the
consequences of those charts of how the public can use charts to basically, you
know, have better lives to be more informed, to be better informed.
JS: But what, when you say that a
graph is being misinterpreted, do you put the onus or the responsibility on the
graph creator or the reader of the graph?
AC: Both. Actually this is a
point, this is a point that I make in the, that I make in the conclusion of the
book in which I say the first responsibility is on the designer. So the
designer needs to make an effort to, you know, try to understand who the
audience is going to be, try to guide the audience, blah, blah, blah, use, you
know, appropriate visualization techniques. If the designer uses a novel
graphic form, explain it so people will understand it. Right? So there’s
obviously a responsibility on the part of the designer, but there is also a
responsibility on the part of the reader. And this is, this is connected to
what I said before about the myths that’s around data visualization. We have
talked, we have been told so many times that our visualization needs to be
intuitive and must be easy to read and simple, et cetera and we have been told
that a picture is should be worth a thousand words and blah blah blah, that we
have internalized that we can understand a visualization just by looking at it
and rather than reading it. And we do need to read it. We need to pay attention.
You cannot assume that you will understand that chart if you don’t read it
carefully. You do need to read it carefully and if you don’t read it carefully
and you misinterpret the chart, if the chart is well designed, then the
responsibility is not the designer’s responsibility. It is your responsibility
as a reader.
JS: Yeah. Where do you place the
responsibility with using the data in this, I guess, process of extracting and
analyzing the data, making the visualization and then publishing it? Where, you
know, how do you separate the data part from the visualization part?
AC: Well, in most cases you
cannot really separate the data from the visualization just because the
visualization is sort of the data mirror made visible or the data made physical
so people can see patterns and transcended data. So the data and the visualization
are intrinsically connected unless, unless you’re doing this as just an aside,
unless that you’re doing that data art project, something that is a little bit
more expressive. In that case, the goal of the visualization is not to
illuminate anything about the data. It’s more to create sort of an aesthetic
experience based on the data. In that case, the data’s a little bit secondary
in comparison to the visual experience, but in most cases when you do a
visualization, the point or the goal of the visualization is to be able to see
something from the data. Whose responsibility is it? Well, it’s the designer’s
responsibility, obviously, to try to get the data right, try to talk to experts
who know much more than you do about the data, etc., etc., to verify what it is
that you are presenting to the extent of your knowledge. At the same time again,
in connecting to what I said before, there is a responsibility on the part of
the, on the part of the reader to try to, you know, read the graphic carefully
and not make assumptions about the graphic. And this is a specific example that
I have in the book that explains this idea well. I will show a chart and let me
say beforehand that this is a, this is a mistake that I have made myself
repeatedly. So there is a chart that shows, it’s a scatterplot that shows a positive
association between cigarette consumption per capita and life expectancy. This
is an example that I borrowed from Heather Cross, who is the statistician and
um, that chart, it shows up positive association. The larger or the bigger is
the cigarette consumption per capita, country by country, the higher the life
expectancy of those countries is, right? So if you’re a cigarette smoker and
you don’t read the… you don’t think about the graphic carefully, you may
describe the content of that chart, the more we smoke, the longer we live,
right? And that’s how, that’s what the chart is showing. What the chart is
showing is that there is a positive association between cigarette consumption
or life expectancy and vice versa, but that doesn’t mean that one of these is
connected to the other in any sense. There’s a problem with correlation
causation, there’s problem with ecological fallacies, there is a problem with
Simpson’s paradoxes and many other things. So readers need to make an effort in
sort of stick to the idea that a chart shows only what it shows and nothing
else. Most of the other inferences that we made out of charts are inferences
that happen in our brain. They are not in the chart itself and that’s a perfect
example of that. Now, in a case
like that, obviously, there’s always, there’s also a huge responsibility on the
part of the designer. If I were to make that chart myself and publish it, I
would add a very big caption warning people about not to see that in the chart.
Right? This chart is not showing that the more that you smoke, the longer you
will live. It just shows that countries that are richer on average can buy more
cigarettes and countries that are richer tend to also have better healthcare
systems. And as a consequence of that and many other factors such as, you know,
diets and exercise and things like that, people also tend to live longer.
JS: Yeah, it’s really
interesting because I think when people see any visual stimuli, they’re,
they’re led to make conclusions or see patterns. Right?
AC: Yeah.
JS: So, so this, this point of
trying to say, hey, don’t, don’t draw a causal link between the two, um, try to
only see, you know, try to only see the correlation, seems like a, I guess it’s
a, it’s kind of a heavy lift, right, for, for designers.
AC: It is a heavy lift. But as I
said, I mean I think that a designer can make an effort to add, you know, if
you are going to publish a chart like that, there may be a good reason that you
want to publish a chart like that. You may want to make a point about that
association for some reason. You know, you can use some space in the chart to
warn people about what the chart is not showing or what, what are the possible
wrong inferences that you can extract from the chart.
JS: Yeah. It’s also a question
of audience, right? Like where do you draw the line of what do I need to
explain to what audience member, right. Like a scatterplot in an economics peer
review journal isn’t going to need a lot of the explanation.
AC: Yeah.
JS: But on the Washington Post
website, it probably does. And so then you have these audiences in between.
AC: Yeah.
JS: Like how do you think about
it? I mean, you’re a journalist. You, you think about different types of
audiences. So how do you think about targeting different audiences and trying
to meet their expertise where they are?
AC: I prefer to put the emphasis
on explanations. So, um, I tried to put myself in a frame of mine in which I
assume that people know a little bit less than I believe they do. Right. So I
try to add more explanations rather than less explanations just to avoid these
kinds of problems. Um, the problem with that obviously that you can end up
having visualizations that are a little bit over burdened with explanations, and
texts, and annotations, et cetera, et cetera. But I’d prefer it that way. I
think that again, as I said, you know, visualization can sometimes lead you to
see patterns that are not really there or, or making inferences that are not
warranted by the, by the data or by the, by the graphic. And I think that it is
worth it to, um, warn people about that. Right. So there is a responsibility on
our end to the…
JS: Yeah, definitely. Let me, I
want to switch gears a little bit and ask about your process. I mean, you’ve
written, let’s see, this is your, what fourth book? You’ve got a couple more in
the works. I think we can talk about those. Um, this one is interesting because
I know you’ve, you, this is a topic of interest you’ve had for a while and
then, uh, was it last year maybe you did your visual trumpery tour where you
sort of, you know, went around the world that looked like and, and talked about
these topics. And I’m curious about your writing process and also how that tour
and your conversations and your presentations affected what you wrote, how you
wrote things that people said to you, you know, what, what was that experience
like?
AC: Yes. The, the tour in the, I
mean, between 2016 and 2018, I did a couple of visual trumpery talks also in
2019, but it mostly, they, mostly all of them took place between 2016 and 2018.
Basically, the talks, these series of presentations, they led to the book,
right? So I first put the slides together, gathered tons of examples that I had
in my computer, added some more, et cetera. And I structured the talk as I
talk, explaining the systematic ways in which either charts are designed to lie
in purpose or the ways in which, in which we mislead ourselves or lie to
ourselves with charts. Um, and I use the talk sort of unconsciously as a way to
test ideas, examples, see how people reacted to those examples, notice what
people understood or didn’t understand in the examples that I was, um, that I
was presenting. And that shaped the, the book, because originally I was
planning to do a book just about line charts. This has, these are just charts
that lie. These are chart that lies for this reason, for another reason. These
are misleading charts for this reason, for another reason. But what I realized
is that that’s not what people need because if you only do that, you’re not
giving people the tools to take advantage of charts. Because the, the title,
the title is How Charts Lie is a provocative title, but the subtitle gives you
a clue of what the book is truly about because the book is not a book about
here, here’s a tons of, a ton of line charts or here are a ton of, a ton of
misleading graphics. The book is a manual about how to become a better chart
reader. So I added a whole chapter that is basically sort of a Grammar of Graphics
light. The famous book, the Grammar of graphics. You know, how our graphic is a
structure. How visualization is a structure? What is visual encoding? I
explained to the general public what visual encoding is, right? So I’ve
involved, you know, tons of pages to basically explain that how charts are red,
right? The same way that we need to teach people how to read words. We can also
teach people how to read visuals, how to read visualization. So the tone of the
book is positive. Originally, the tone was a slightly negative, right? This is
not, this is bullshit. This is a bad chart. This is whatever. And there’s
certainly something loud, something about that in the book itself. Um, there
are plenty of examples that are really, really bad, but most of the book has a
very positive tone. It’s not, you know, it is not, the book doesn’t just say
charts mislead us very often. It also says, but charts can be used to make us
smarter, to make us a better, better human beings and more informed. And this
is how to [indiscernible 00:22:08] I have to do it.
JS: I want to, I want to make
you king of the world for a moment or at least king of the education system?
Um, what would you change in the way people learn how to read charts from
kindergarten all the way through, uh, through college? Like how would you
change the curriculum?
AC: Well, you cannot really, I
don’t think that we can really detach, um, graphical literacy or graphicacy
from numerical literacy, also called numeracy. There’s a famous book title Innumeracy
by John Allen Paulos, which is fantastic. It’s a fantastic book. I think that
both things go hand in hand. We need to help people become more numerate,
become more used to dealing with numbers or reason based on, based on numbers
and, and then we’re going to also teach people, help people become more
visually literate, more graphicate. Right? Those things go hand in hand. Now
how to do it? I have no idea. I mean, I don’t know. I’m not an educator, but
I’m not used to teaching small children. The way that, the way that perhaps we
could do it in math classes is to spend a little bit less time making children,
you know, do complex calculations by hand and spend more time discussing how
the numbers that they see every day in the classroom apply or relate to their,
to their lives. Uh, perhaps, perhaps use examples that speak to them. So more
examples about music or movies or things like that. And then talk about, you
know, how to reason about the numbers, about the, related to those topics that
they care about, right? What is the album or the song that has sold more copies
in the past? What is the song that has made more money in the past 10 years?
Right? And you can use that to explain, I don’t know, adjusting for inflation,
right? A song that was published this year obviously will make much more money
than a song that was published 20 years ago, but the, that’s just an effect of,
of inflation rate. If you’d done adjustment for inflation, then it will appear
that way. So you can use that as example, as example, as an entry point to
explain a complex idea or a complex issue. But again, this is just a very
general idea. I don’t know, I just think that I, I’m more fond about the
classes that sort of expand your mind by helping you see the multiple angles in
which you can approach a topic rather, rather than just teaching people how to
make comically complete operations by hand, which I also believe is necessary.
It is necessary to multiply, but after you have done that 10 times, just [indiscernible
00:24:48] calculator.
JS: Well, what’s interesting
about the, about the field of data visualization, right, is that it brings a
lot of these different skill sets together. You’ve got the math and you’ve got
the literature and you’ve got design and art and um, you’ve got even, you know,
computer science. It’s bringing all these different skill sets and philosophies
together into one area.
AC: Yeah. And not only
quantitative fields, it also brings together, you know, rhetoric and journalism
and narrative and the storytelling. It’s like, it’s a bunch of stuff, right?
Yup.
JS: Yeah. So would you change
the way people are taught visuals at the university level?
AC: Yeah, absolutely. So, um,
actually this may inform, um, this idea may inform, um, one of the books that I
have planned for the near future. Um, it’s a still a little bit vague in my
mind. Um, but I would like to follow the path of Andy Kirk. You know that Andy
wrote his book, um, with the idea that visualization is a process, right? It’s
not…
JS: Right.
AC: Yes. Just go deeper into that
idea and write a book that talks about how to reason about visualization, how
to make good decisions about visualization, not by applying cookie cutter
rules, right? Which is how visualization is usually taught. Here’s a bar chart.
Here’s a bar chart for these. Here’s a scatterplot. Here is a scatterplot for
these and go deeper into the reasoning behind all of those rules. And that way
I think that people will understand better when the rule is applicable and when
the rule needs to be broken or when the rule can be basically just avoided or
how to create new rules and how to expand the vocabulary of data visualization.
So how, how to think about visualization, how to reason about visualization or
how visualization designers currently think, right. That will be another way in
which, um, in which people can learn. So I think that that’s the way to teach
visualization at the moment to anybody who wants to learn it.
JS: Do you think the, the data viz
field is, is pivoting in that direction in terms of what people are speaking
about and writing about on blogs and on and on websites?
AC: Um, people who have been in
the field for, for a relatively long time, absolutely. They are pivoting in
that direction. Yeah. The field is pivoting in that direction. Um, but I don’t
care that much about the people who have a lot of experience, right. They are
autonomous in their own, right. I’m more worried about other people who are
entering the field at this moment, right. We need, I think to find the balance
between saying, you know, there are certain, um, rules, quotation mark in
there, there are certain principles, there are certain conventions, there is
that tradition in data visualization and you need to respect all that because
there is a reason why all these things exist. But at the same time, it is also
important to understand where all these conventions, principles and rules come
from, which one of them are more or less supported by either evidence or logic
or practice, etc., um, learn how they were developed, etc., and then learn how
to break them or how to expand them or how to create new ones in the future, right.
So, yeah, we are pivoting in that direction, but I think that we need to pivot
perhaps a little bit more.
JS: Hmm. Interesting. I have the
reading copy here in front of me and uh, I’ve been going through it again, I
think this is like the third time I’ve read it. It’s, uh, it’s great. I’m
really enjoying it and, um, I look forward to seeing it come out and, and make
its ways around the world and see how, see what people say about it.
AC: Thank you, Jon. You’re very,
very kind.
JS: Well, thanks Alberto. Always
fun to chat with you.
[Music]
JS: And thanks to everyone for
tuning into this week’s episode. I hope you enjoyed it. I hope you’ll check out
Alberto’s new book, How Charts Lie. Uh, it is coming out any day now. Um, and
if you’re interested in seeing Alberto speak, uh, in person, he’ll be at the Urban
Institute in October, uh, to talk about his book. Um, so, uh, stay tuned for
information on that. That’ll be coming out in a little while. Um, and if you’d
like to support this podcast, please check out my Patrion page or just share
the show with, uh, your friends and your colleagues and review the show on your
favourite podcast provider. So until next time, this has been the PolicyViz
Podcast. Thanks so much for listening.
[Music]
The post Episode #157: Alberto Cairo appeared first on Policy Viz.
.........

isusing data and charts to sort of prove quotation marks in there
that African Americans target whites more often than whites target African
Americans when it comes to committing a crime or when white, when black
criminals target white people, that happens more often than white criminals
targeting black people. And I’m not going to get into the details of why, you
know, these, all these graphics are, are basically um, crap. Uh, I describe
that in a lot of detail in the book. Um, but it’s like it’s a very sad story
and actually demonstrates that bad charts can sometimes have terrible
consequences. So this guy Roof will be a racist regardless of the existence of
these graphics I believe because he was a racist since he was a, since he was a
child. But I think that the charts contributed to basically ground his beliefs
and strengthen his beliefs even more. So cases like these are certainly
infuriating and there are a few other charts in the book that I believe that
were designed intentionally to mislead people. And I called them out obviously,
but most of the examples in the book are examples of charts that are otherwise
perfectly designed, but they are often misread or misinterpreted. And this is
not infuriating. It’s just a fact of life. I mean, we are taught, you know, a
or we are, we are told that, you know, we should be able to intuitively
understand visualization that visualization is easy to read, that a picture is
worth a thousand words and things like that. And in the book I tried to
demonstrate that all these myths are actually myths and that we need to abandon
them that visualization is sort of like a like written language. You need to
pay attention to it. You need to read it carefully, uh, in order to interpret
it, uh, in order to interpret.
JS: Yeah. Yeah. I’m always
surprised when I show people, you know, a different type of graph. You know, like,
you know what we, you know, like a slope chart or a dot plot that we have of,
you know, that we in the field know now instinctively. And people say I can’t
show this to my boss or my manager or whatever because they’ll never understand
it. And I find that interesting because it’s not like we know instinctively in
our DNA how to read a bar chart. We have to learn how to read a bar chart.
AC: Yeah. I, I perfectly remember
when I learned to use a, to, to read a scatterplot for example, and it was not
intuitive. I needed to pay attention to the chart. Take a look at the axis, um,
read a little description, read a little caption that the chart had in order to
interpret it correctly. So in the book I talk about visual literacy obviously.
There is a term to refer to visualization literacy. The term is graphicacy. And
I explained where, where that term comes from and other authors that have used
it in the past. And I said that the problem is that we like graphicacy and but
we can increase graphicacy. The, the problem with, you know, people who react
negatively to novel graphic forms is that they say, well, my reader is not
going to understand this chart. So they refrain from using that chart. But
that’s the wrong response. The wrong response is to say, well, if this is the
best chart to represent your data or to tell or to convey the message that you
want to convey, do use it. Go ahead and use it, but also explain how to read it
right to help guide your readers by the hand in order for them to understand
what’s going on in that chart. And then the next time that they see that same
type of chart, they would be able to read it on their own. You have, you would
have increased their graphicacy.
JS: Yeah. Yeah, absolutely. So,
so you talked about a couple of these examples where the graph was
intentionally misleading or misrepresenting the data. What’s the balance
between those type of charts that are intentionally misleading versus one that,
that use bad data visualization techniques?
AC: Well, in the book itself, in
How Charts Lie, I would say that 20, 25% of the examples are charts that I
guess, it’s just a guess that I guess that are intentionally misleading. Um,
and the other 75% are charts that are either well-designed, but misinterpreted
anyway, or charts that are designed with good intentions in mind, but that they
employ, you know, visualization techniques that are not appropriate for a
particular audience. And as a consequence of that, they end up being misleading
any way. The result is [indiscernible 00:10:46]. The audience, audience that is
reading their graphic is misled. So I would say that that’s sort of the sort of
the balance because again, I’m much more interested not in the intentions of
the designers who create those charts. I’m much more interested in the
consequences of those charts of how the public can use charts to basically, you
know, have better lives to be more informed, to be better informed.
JS: But what, when you say that a
graph is being misinterpreted, do you put the onus or the responsibility on the
graph creator or the reader of the graph?
AC: Both. Actually this is a
point, this is a point that I make in the, that I make in the conclusion of the
book in which I say the first responsibility is on the designer. So the
designer needs to make an effort to, you know, try to understand who the
audience is going to be, try to guide the audience, blah, blah, blah, use, you
know, appropriate visualization techniques. If the designer uses a novel
graphic form, explain it so people will understand it. Right? So there’s
obviously a responsibility on the part of the designer, but there is also a
responsibility on the part of the reader. And this is, this is connected to
what I said before about the myths that’s around data visualization. We have
talked, we have been told so many times that our visualization needs to be
intuitive and must be easy to read and simple, et cetera and we have been told
that a picture is should be worth a thousand words and blah blah blah, that we
have internalized that we can understand a visualization just by looking at it
and rather than reading it. And we do need to read it. We need to pay attention.
You cannot assume that you will understand that chart if you don’t read it
carefully. You do need to read it carefully and if you don’t read it carefully
and you misinterpret the chart, if the chart is well designed, then the
responsibility is not the designer’s responsibility. It is your responsibility
as a reader.
JS: Yeah. Where do you place the
responsibility with using the data in this, I guess, process of extracting and
analyzing the data, making the visualization and then publishing it? Where, you
know, how do you separate the data part from the visualization part?
AC: Well, in most cases you
cannot really separate the data from the visualization just because the
visualization is sort of the data mirror made visible or the data made physical
so people can see patterns and transcended data. So the data and the visualization
are intrinsically connected unless, unless you’re doing this as just an aside,
unless that you’re doing that data art project, something that is a little bit
more expressive. In that case, the goal of the visualization is not to
illuminate anything about the data. It’s more to create sort of an aesthetic
experience based on the data. In that case, the data’s a little bit secondary
in comparison to the visual experience, but in most cases when you do a
visualization, the point or the goal of the visualization is to be able to see
something from the data. Whose responsibility is it? Well, it’s the designer’s
responsibility, obviously, to try to get the data right, try to talk to experts
who know much more than you do about the data, etc., etc., to verify what it is
that you are presenting to the extent of your knowledge. At the same time again,
in connecting to what I said before, there is a responsibility on the part of
the, on the part of the reader to try to, you know, read the graphic carefully
and not make assumptions about the graphic. And this is a specific example that
I have in the book that explains this idea well. I will show a chart and let me
say beforehand that this is a, this is a mistake that I have made myself
repeatedly. So there is a chart that shows, it’s a scatterplot that shows a positive
association between cigarette consumption per capita and life expectancy. This
is an example that I borrowed from Heather Cross, who is the statistician and
um, that chart, it shows up positive association. The larger or the bigger is
the cigarette consumption per capita, country by country, the higher the life
expectancy of those countries is, right? So if you’re a cigarette smoker and
you don’t read the… you don’t think about the graphic carefully, you may
describe the content of that chart, the more we smoke, the longer we live,
right? And that’s how, that’s what the chart is showing. What the chart is
showing is that there is a positive association between cigarette consumption
or life expectancy and vice versa, but that doesn’t mean that one of these is
connected to the other in any sense. There’s a problem with correlation
causation, there’s problem with ecological fallacies, there is a problem with
Simpson’s paradoxes and many other things. So readers need to make an effort in
sort of stick to the idea that a chart shows only what it shows and nothing
else. Most of the other inferences that we made out of charts are inferences
that happen in our brain. They are not in the chart itself and that’s a perfect
example of that. Now, in a case
like that, obviously, there’s always, there’s also a huge responsibility on the
part of the designer. If I were to make that chart myself and publish it, I
would add a very big caption warning people about not to see that in the chart.
Right? This chart is not showing that the more that you smoke, the longer you
will live. It just shows that countries that are richer on average can buy more
cigarettes and countries that are richer tend to also have better healthcare
systems. And as a consequence of that and many other factors such as, you know,
diets and exercise and things like that, people also tend to live longer.
JS: Yeah, it’s really
interesting because I think when people see any visual stimuli, they’re,
they’re led to make conclusions or see patterns. Right?
AC: Yeah.
JS: So, so this, this point of
trying to say, hey, don’t, don’t draw a causal link between the two, um, try to
only see, you know, try to only see the correlation, seems like a, I guess it’s
a, it’s kind of a heavy lift, right, for, for designers.
AC: It is a heavy lift. But as I
said, I mean I think that a designer can make an effort to add, you know, if
you are going to publish a chart like that, there may be a good reason that you
want to publish a chart like that. You may want to make a point about that
association for some reason. You know, you can use some space in the chart to
warn people about what the chart is not showing or what, what are the possible
wrong inferences that you can extract from the chart.
JS: Yeah. It’s also a question
of audience, right? Like where do you draw the line of what do I need to
explain to what audience member, right. Like a scatterplot in an economics peer
review journal isn’t going to need a lot of the explanation.
AC: Yeah.
JS: But on the Washington Post
website, it probably does. And so then you have these audiences in between.
AC: Yeah.
JS: Like how do you think about
it? I mean, you’re a journalist. You, you think about different types of
audiences. So how do you think about targeting different audiences and trying
to meet their expertise where they are?
AC: I prefer to put the emphasis
on explanations. So, um, I tried to put myself in a frame of mine in which I
assume that people know a little bit less than I believe they do. Right. So I
try to add more explanations rather than less explanations just to avoid these
kinds of problems. Um, the problem with that obviously that you can end up
having visualizations that are a little bit over burdened with explanations, and
texts, and annotations, et cetera, et cetera. But I’d prefer it that way. I
think that again, as I said, you know, visualization can sometimes lead you to
see patterns that are not really there or, or making inferences that are not
warranted by the, by the data or by the, by the graphic. And I think that it is
worth it to, um, warn people about that. Right. So there is a responsibility on
our end to the…
JS: Yeah, definitely. Let me, I
want to switch gears a little bit and ask about your process. I mean, you’ve
written, let’s see, this is your, what fourth book? You’ve got a couple more in
the works. I think we can talk about those. Um, this one is interesting because
I know you’ve, you, this is a topic of interest you’ve had for a while and
then, uh, was it last year maybe you did your visual trumpery tour where you
sort of, you know, went around the world that looked like and, and talked about
these topics. And I’m curious about your writing process and also how that tour
and your conversations and your presentations affected what you wrote, how you
wrote things that people said to you, you know, what, what was that experience
like?
AC: Yes. The, the tour in the, I
mean, between 2016 and 2018, I did a couple of visual trumpery talks also in
2019, but it mostly, they, mostly all of them took place between 2016 and 2018.
Basically, the talks, these series of presentations, they led to the book,
right? So I first put the slides together, gathered tons of examples that I had
in my computer, added some more, et cetera. And I structured the talk as I
talk, explaining the systematic ways in which either charts are designed to lie
in purpose or the ways in which, in which we mislead ourselves or lie to
ourselves with charts. Um, and I use the talk sort of unconsciously as a way to
test ideas, examples, see how people reacted to those examples, notice what
people understood or didn’t understand in the examples that I was, um, that I
was presenting. And that shaped the, the book, because originally I was
planning to do a book just about line charts. This has, these are just charts
that lie. These are chart that lies for this reason, for another reason. These
are misleading charts for this reason, for another reason. But what I realized
is that that’s not what people need because if you only do that, you’re not
giving people the tools to take advantage of charts. Because the, the title,
the title is How Charts Lie is a provocative title, but the subtitle gives you
a clue of what the book is truly about because the book is not a book about
here, here’s a tons of, a ton of line charts or here are a ton of, a ton of
misleading graphics. The book is a manual about how to become a better chart
reader. So I added a whole chapter that is basically sort of a Grammar of Graphics
light. The famous book, the Grammar of graphics. You know, how our graphic is a
structure. How visualization is a structure? What is visual encoding? I
explained to the general public what visual encoding is, right? So I’ve
involved, you know, tons of pages to basically explain that how charts are red,
right? The same way that we need to teach people how to read words. We can also
teach people how to read visuals, how to read visualization. So the tone of the
book is positive. Originally, the tone was a slightly negative, right? This is
not, this is bullshit. This is a bad chart. This is whatever. And there’s
certainly something loud, something about that in the book itself. Um, there
are plenty of examples that are really, really bad, but most of the book has a
very positive tone. It’s not, you know, it is not, the book doesn’t just say
charts mislead us very often. It also says, but charts can be used to make us
smarter, to make us a better, better human beings and more informed. And this
is how to [indiscernible 00:22:08] I have to do it.
JS: I want to, I want to make
you king of the world for a moment or at least king of the education system?
Um, what would you change in the way people learn how to read charts from
kindergarten all the way through, uh, through college? Like how would you
change the curriculum?
AC: Well, you cannot really, I
don’t think that we can really detach, um, graphical literacy or graphicacy
from numerical literacy, also called numeracy. There’s a famous book title Innumeracy
by John Allen Paulos, which is fantastic. It’s a fantastic book. I think that
both things go hand in hand. We need to help people become more numerate,
become more used to dealing with numbers or reason based on, based on numbers
and, and then we’re going to also teach people, help people become more
visually literate, more graphicate. Right? Those things go hand in hand. Now
how to do it? I have no idea. I mean, I don’t know. I’m not an educator, but
I’m not used to teaching small children. The way that, the way that perhaps we
could do it in math classes is to spend a little bit less time making children,
you know, do complex calculations by hand and spend more time discussing how
the numbers that they see every day in the classroom apply or relate to their,
to their lives. Uh, perhaps, perhaps use examples that speak to them. So more
examples about music or movies or things like that. And then talk about, you
know, how to reason about the numbers, about the, related to those topics that
they care about, right? What is the album or the song that has sold more copies
in the past? What is the song that has made more money in the past 10 years?
Right? And you can use that to explain, I don’t know, adjusting for inflation,
right? A song that was published this year obviously will make much more money
than a song that was published 20 years ago, but the, that’s just an effect of,
of inflation rate. If you’d done adjustment for inflation, then it will appear
that way. So you can use that as example, as example, as an entry point to
explain a complex idea or a complex issue. But again, this is just a very
general idea. I don’t know, I just think that I, I’m more fond about the
classes that sort of expand your mind by helping you see the multiple angles in
which you can approach a topic rather, rather than just teaching people how to
make comically complete operations by hand, which I also believe is necessary.
It is necessary to multiply, but after you have done that 10 times, just [indiscernible
00:24:48] calculator.
JS: Well, what’s interesting
about the, about the field of data visualization, right, is that it brings a
lot of these different skill sets together. You’ve got the math and you’ve got
the literature and you’ve got design and art and um, you’ve got even, you know,
computer science. It’s bringing all these different skill sets and philosophies
together into one area.
AC: Yeah. And not only
quantitative fields, it also brings together, you know, rhetoric and journalism
and narrative and the storytelling. It’s like, it’s a bunch of stuff, right?
Yup.
JS: Yeah. So would you change
the way people are taught visuals at the university level?
AC: Yeah, absolutely. So, um,
actually this may inform, um, this idea may inform, um, one of the books that I
have planned for the near future. Um, it’s a still a little bit vague in my
mind. Um, but I would like to follow the path of Andy Kirk. You know that Andy
wrote his book, um, with the idea that visualization is a process, right? It’s
not…
JS: Right.
AC: Yes. Just go deeper into that
idea and write a book that talks about how to reason about visualization, how
to make good decisions about visualization, not by applying cookie cutter
rules, right? Which is how visualization is usually taught. Here’s a bar chart.
Here’s a bar chart for these. Here’s a scatterplot. Here is a scatterplot for
these and go deeper into the reasoning behind all of those rules. And that way
I think that people will understand better when the rule is applicable and when
the rule needs to be broken or when the rule can be basically just avoided or
how to create new rules and how to expand the vocabulary of data visualization.
So how, how to think about visualization, how to reason about visualization or
how visualization designers currently think, right. That will be another way in
which, um, in which people can learn. So I think that that’s the way to teach
visualization at the moment to anybody who wants to learn it.
JS: Do you think the, the data viz
field is, is pivoting in that direction in terms of what people are speaking
about and writing about on blogs and on and on websites?
AC: Um, people who have been in
the field for, for a relatively long time, absolutely. They are pivoting in
that direction. Yeah. The field is pivoting in that direction. Um, but I don’t
care that much about the people who have a lot of experience, right. They are
autonomous in their own, right. I’m more worried about other people who are
entering the field at this moment, right. We need, I think to find the balance
between saying, you know, there are certain, um, rules, quotation mark in
there, there are certain principles, there are certain conventions, there is
that tradition in data visualization and you need to respect all that because
there is a reason why all these things exist. But at the same time, it is also
important to understand where all these conventions, principles and rules come
from, which one of them are more or less supported by either evidence or logic
or practice, etc., um, learn how they were developed, etc., and then learn how
to break them or how to expand them or how to create new ones in the future, right.
So, yeah, we are pivoting in that direction, but I think that we need to pivot
perhaps a little bit more.
JS: Hmm. Interesting. I have the
reading copy here in front of me and uh, I’ve been going through it again, I
think this is like the third time I’ve read it. It’s, uh, it’s great. I’m
really enjoying it and, um, I look forward to seeing it come out and, and make
its ways around the world and see how, see what people say about it.
AC: Thank you, Jon. You’re very,
very kind.
JS: Well, thanks Alberto. Always
fun to chat with you.
[Music]
JS: And thanks to everyone for
tuning into this week’s episode. I hope you enjoyed it. I hope you’ll check out
Alberto’s new book, How Charts Lie. Uh, it is coming out any day now. Um, and
if you’re interested in seeing Alberto speak, uh, in person, he’ll be at the Urban
Institute in October, uh, to talk about his book. Um, so, uh, stay tuned for
information on that. That’ll be coming out in a little while. Um, and if you’d
like to support this podcast, please check out my Patrion page or just share
the show with, uh, your friends and your colleagues and review the show on your
favourite podcast provider. So until next time, this has been the PolicyViz
Podcast. Thanks so much for listening.
[Music]
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